Experimental realization of arbitrary activation functions for optical neural networks
Optics Express, 2020 We experimentally demonstrate an on-chip electro-optic circuit for realizing arbitrary nonlinear activation functions for optical neural networks (ONNs).
Tyler Hughes +2 more
exaly +2 more sources
Image sensing with multilayer nonlinear optical neural networks [PDF]
Nature Photonics, 2022 A nonlinear optical neural network image sensor based on an image intensifier enables efficient all-optical image encoding for a variety of machine-vision tasks.
Tianyu Wang +7 more
semanticscholar +1 more source
Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks [PDF]
Nature Communications, 2023 Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and ...
Chu-Yu Zhong +20 more
semanticscholar +1 more source
Optical Neural Network in Free-Space and Nanophotonics
IEEE Access, 2023 The explosive data growth has resulted in increased computing costs. As Moore’s Law is increasingly slowing down, the traditional computing approach based on the von Neumann architecture is gradually becoming unable to fulfill future computing ...
Zhenlin Sun +5 more
doaj +1 more source
The role of all-optical neural networks [PDF]
arXiv.org, 2023 In light of recent achievements in optical computing and machine learning, we consider the conditions under which all-optical computing may surpass electronic and optoelectronic computing in terms of energy efficiency and scalability.
M. Matuszewski, A. Prystupiuk, A. Opala
semanticscholar +1 more source
Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
doaj +1 more source
Redundancy-free integrated optical convolver for optical neural networks based on arrayed waveguide grating [PDF]
Nanophotonics, 2023 Optical neural networks (ONNs) have gained significant attention due to their potential for high-speed and energy-efficient computation in artificial intelligence.
Shijing Zhang +6 more
semanticscholar +1 more source
Low-depth optical neural networks
Chip, 2022 Optical neural network (ONNs) are emerging as attractive proposals for machine-learning applications. However, the stability of ONNs decreases with the circuit depth, limiting the scalability of ONNs for practical uses.
Xiao-Ming Zhang, Man-Hong Yung
doaj +1 more source
Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits
Photonics, 2021 We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation.
Qi Zhang, Zhuangzhuang Xing, Duan Huang
doaj +1 more source
Opto-Electronic Hybrid Network Based on Scattering Layers
Sensors, 2023 Owing to the disparity between the computing power and hardware development in electronic neural networks, optical diffraction networks have emerged as crucial technologies for various applications, including target recognition, because of their high ...
Jiakang Zhu +4 more
doaj +1 more source